Heart failure (HF) affects nearly 6 million individuals in the US, one of every eight death certificates lists HF as a primary or contributing cause of death, and HF is associated with a significant reduction in quality of life for patients and caregivers alike. Cardiac resynchronization therapy (CRT) has represented one of the most important advances in the care of select patients with HF. Although CRT has substantially improved outcomes among HF patients, it is widely recognized that approximately 1/3 of patients who undergo this invasive procedure do not derive clinical improvements. Research on causes of CRT non-response and approaches to mitigating non-response is a priority in the fields of HF and electrophysiology. Although many important questions can be answered through national registries and secondary analyses of landmark trials, lack of data granularity and adjudicated outcomes in registries, and small numbers of patients in key subgroups in trials, limit these approaches. Our group has successfully used Bayesian statistical methods to combine primary patient-level data from RCTs of the implantable cardioverter defibrillator. We propose that a similar approach for CRT trials, combined with stakeholder prioritization and a decision modeling framework, have the power to overcome many important limitations of existing platforms for CRT research. Our long-term goal is to enhance the ability of the NHLBI to provide evidence-based decisionmaking tools to aid patients, providers, and policymakers in the use of CRT for the treatment of HF. To achieve this overall goal, we have four specific aims (SA): (1) To work with diverse stakeholders to identify and prioritize timely clinical and policy questions regarding the comparative effectiveness of CRT; (2) To develop a generalizable decision modeling framework for the treatment of HF; (3) To use Bayesian statistical techniques to devise a model for predicting patient and population health and economic outcomes; and (4) To combine the framework from SA#2, the Bayesian model from SA#3, and patient level data from existing RCTs and registries, to explore high-priority questions identified in SA#1. Our research will build off our team's expertise and experience in research prioritization, evidence synthesis, chronic disease modeling, Bayesian statistical techniques, stakeholder engagement, and methods of disseminating evidence-based decision models to patients, providers, and policymakers. We will collaborate with principal investigators from existing trials of CRT to harness the power of patient-level data from over ten years of clinical trials representing nearly 10,000 patients and use registry data to explore whether the outcomes observed in the community are predicted by available RCT evidence. In an era in which great importance is placed on defending clinical practice with rigorous supporting evidence, our research brings together stakeholder engagement, decision analytic methods, Bayesian statistics, the strength of RCT data, and medical informatics tools to enhance patient outcomes and provide powerful methods to aid patients, providers, and policy makers in their decisionmaking.